The objective of this industry study is to shed light on the current situation and improvement needs in software test automation. To this end, 55 industry specialists from 31 organizational units were interviewed. In parallel with the survey, a qualitative study was conducted in 12 selected software development organizations. The results indicated that the software testing processes usually follow systematic methods to a large degree, and have only little immediate or critical requirements for resources. Based on the results, the testing processes have approximately three fourths of the resources they need, and have access to a limited, but usually sufficient, group of testing tools. As
Personalization is an upcoming trend in gamification research, with several researchers proposing that gamified systems should take personal characteristics into account. However, creating good gamified designs is effort intensive as it is and tailoring system interactions to each user will only add to this workload. We propose machine learning algorithm-based personalized content selection to address a part of this problem and present a process for creating personalized designs that allows automating a part of the implementation. The process is based on Deterding's 2015 framework for gameful design, the lens of intrinsic skill atoms, with additional steps for selecting a personalization strategy and algorithm creation. We then demonstrate the process by implementing personalized gamification for a computer-supported collaborative learning environment. For this demonstration, we use the gamification user type hexad for personalization and the heuristics for effective design of gamification for overall design. The result of the applied design process is a context-aware, personalized gamification ruleset for collaborative environments. Lastly, we present a method for translating gamification rulesets to machine-readable classifier algorithm using the CN2 rule inducer.
It has been estimated that more than two million students started computing studies in 1999 and 650,000 of them either dropped or failed their first programming course. For the individual student, dropping such a course can distract from the completion of later courses in a computing curriculum and may even result in changing their course of study to a curriculum without programming. In this article, we report on how we set out to rehabilitate a troubled first programming course, one for which the dropout statistic and repercussion was evident. The five-year longitudinal case study described in this article began by systematically tracking the pass rate of a first programming course, its throughput, as proposed by the Theory of Constraints. The analyses of these data indicated three main problems in the course: programming discipline difficulty, course arrangement complexity, and limited student motivation. The motivation problem was approached from the Two-Factor Theory point of view. It investigated those factors that led to dissatisfaction among the students, the hygiene factors, and those factors that led to satisfaction, the intrinsic and extrinsic motivators. The course arrangement complexity was found to be a hygiene factor, while the lack of extrinsic and intrinsic motivators contributed to the high dropout rates. The course improvement efforts made no attempt to change the inherent characteristics of the programming discipline, but introduced holistic changes in the course arrangements over a five-year period, from 2005 to 2009, to eliminate the hygiene factors and to increase motivational aspects of the course. This systems approach to course improvement resulted in an increase in the pass rate, from 44% prior to the changes to 68% thereafter, and the overall course atmosphere turned positive. This paper reports on the detailed changes that were made and the improvements that were achieved over this five-year period.
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